Structural graphical lasso for learning mouse brain connectivity

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn 2015-August, Issue , 2015, Pages 1385-1394

  Yang, Sen ^a   Sun, Qian ^b   Ji, Shuiwang ^c   Wonka, Peter ^d   Davidson, Ian ^e   Ye, Jieping ^f  

^a ALIBABA GROUP   (United States)

^b ARIZONA STATE UNIVERSITY   (United States)

^c OLD DOMINION UNIVERSITY   (United States)

^d KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Brain networks; Graphical lasso; Proximal operator; Screening; Second order method; Tree structural regularization

Indexed keywords

ALGORITHMS; BRAIN; BRAIN MAPPING; CLUSTERING ALGORITHMS; FORESTRY; GRAPHIC METHODS; INVERSE PROBLEMS; MAMMALS; NEUROIMAGING; SCREENING;

BRAIN NETWORKS; GRAPHICAL LASSOS; PROXIMAL OPERATOR; SECOND-ORDER METHODS; TREE-STRUCTURAL REGULARIZATION;

DATA MINING;

EID: 84954170577     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2783258.2783391     Document Type: Conference Paper

References (36)

1. Allen Developing Mouse Brain Atlas. http://developingmouse.brain-map.org. 2013.
2. Allen Institute for Brain Science. Technical White Paper: Informatics Data Processing for the Allen Developing Mouse Brain Atlas, 2012.
3. Francis Bach, Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, et al. Structured sparsity through convex optimization. Statistical Science, 27(4):450-468, 2012.
4. O. Banerjee, L. El Ghaoui, and A. d'Aspremont. Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data. The Journal of Machine Learning Research, 9:485-516, 2008.
5. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning, 3(1):1-122, 2011.
6. H.Y. Chuang, E. Lee, Y.T. Liu, D. Lee, and T. Ideker. Network-based classification of breast cancer metastasis. Molecular systems biology, 3(1), 2007.
7. Q. Dinh, A. Kyrillidis, and V. Cevher. A proximal newton framework for composite minimization: Graph learning without cholesky decompositions and matrix inversions. arXiv preprint arXiv:1301.1459, 2013.
8. J. Friedman, T. Hastie, and R. Tibshirani. Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3):432-441, 2008.
9. Karl J Friston. Functional and effective connectivity: a review. Brain connectivity, 1(1):13-36, 2011.
10. J. Guo, E. Levina, G. Michailidis, and J. Zhu. Joint estimation of multiple graphical models. Biometrika, 98(1):1-15, 2011.
11. C.J. Hsieh, M. A. Sustik, I. S. Dhillon, and P. Ravikumar. Sparse inverse covariance matrix estimation using quadratic approximation. Advances in Neural Information Processing Systems (NIPS), 24, 2011.
12. S. Huang, J. Li, L. Sun, J. Liu, T. Wu, K. Chen, A. Fleisher, E. Reiman, and J. Ye. Learning brain connectivity of alzheimer's disease from neuroimaging data. In Advances in Neural Information Processing Systems (NIPS), pages 808-816, 2009.
13. M. Kolar, H. Liu, and E. Xing. Markov network estimation from multi-attribute data. In Proceedings of The 30th International Conference on Machine Learning, pages 73-81, 2013.
14. J. D. Lee, Y. Sun, and M. A. Saunders. Proximal newton-type methods for minimizing convex objective functions in composite form. arXiv preprint arXiv:1206.1623, 2012.
15. L. Li and K.C. Toh. An inexact interior point method for l 1-regularized sparse covariance selection. Mathematical Programming Computation, 2(3):291-315, 2010.
16. H. Liu, K. Roeder, and L. Wasserman. Stability approach to regularization selection (StARS) for high dimensional graphical models. In Advances in Neural Information Processing Systems (NIPS), 2011.
17. J. Liu, S. Ji, and J. Ye. SLEP: Sparse Learning with Efficient Projections. Arizona State University, 2009.
18. J. Liu and J. Ye. Moreau-yosida regularization for grouped tree structure learning. In Advances in Neural Information Processing Systems (NIPS), pages 1459-1467, 2010.
19. Z. Lu. Smooth optimization approach for sparse covariance selection. SIAM Journal on Optimization, 19(4):1807-1827, 2009.
20. X. Luo. A hierarchical graphical model for big inverse covariance estimation with an application to fmri. arXiv preprint arXiv:1403.4698, 2014.
21. R. Mazumder and T. Hastie. Exact covariance thresholding into connected components for large-scale graphical lasso. The Journal of Machine Learning Research, 13:781-794, 2012.
22. R. Mazumder and T. Hastie. The graphical lasso: New insights and alternatives. Electronic Journal of Statistics, 6:2125-2149, 2012.
23. N. Meinshausen and P. Bühlmann. High-dimensional graphs and variable selection with the lasso. The Annals of Statistics, 34(3):1436-1462, 2006.
24. P. A. Olsen, F. Oztoprak, J. Nocedal, and S. J. Rennie. Newton-like methods for sparse inverse covariance estimation. In Advances in Neural Information Processing Systems (NIPS), 2012.
25. K. Scheinberg, S. Ma, and D. Goldfarb. Sparse inverse covariance selection via alternating linearization methods. In Advances in Neural Information Processing Systems (NIPS), 2010.
26. Carol L Thompson, Lydia Ng, Vilas Menon, Salvador Martinez, Chang-Kyu Lee, Katie Glattfelder, Susan M Sunkin, Alex Henry, Christopher Lau, Chinh Dang, et al. A high-resolution spatiotemporal atlas of gene expression of the developing mouse brain. Neuron, 83(2):309-323, 2014.
27. R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), pages 267-288, 1996.
28. P. Tseng and S. Yun. A coordinate gradient descent method for nonsmooth separable minimization. Mathematical Programming, 117(1):387-423, 2009.
29. C. Wang, D. Sun, and K.C. Toh. Solving log-determinant optimization problems by a newton-cg primal proximal point algorithm. SIAM Journal on Optimization, 20(6):2994-3013, 2010.
30. D. M. Witten, J. H. Friedman, and N. Simon. New insights and faster computations for the graphical lasso. Journal of Computational and Graphical Statistics, 20(4):892-900, 2011.
31. S. J. Wright, R. D. Nowak, and M.A.T. Figueiredo. Sparse reconstruction by separable approximation. Signal Processing, IEEE Transactions on, 57(7):2479-2493, 2009.
32. S. Yang, Z. Lu, X. Shen, P. Wonka, and J. Ye. Fused multiple graphical lasso. arXiv preprint arXiv:1209.2139v2, 2013.
33. S. Yang, L. Yuan, Y.C. Lai, X. Shen, P. Wonka, and J. Ye. Feature grouping and selection over an undirected graph. In KDD, pages 922-930. ACM, 2012.
34. J. Ye and J. Liu. Sparse methods for biomedical data. ACM SIGKDD Explorations Newsletter, 14(1):4-15, 2012.
35. M. Yuan and Y. Lin. Model selection and estimation in the gaussian graphical model. Biometrika, 94(1):19-35, 2007.
36. X. Yuan. Alternating direction method for covariance selection models. Journal of Scientific Computing, 51(2):261-273, 2012.